!4865 Rewrite the REAMD.md in Wide&Deep model
Merge pull request !4865 from huangxinjing/wide-deep-readme
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Recommendation Model
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## Overview
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This is an implementation of WideDeep as described in the [Wide & Deep Learning for Recommender System](https://arxiv.org/pdf/1606.07792.pdf) paper.
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# Contents
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- [Wide&Deep Description](#widedeep-description)
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- [Model Architecture](#model-architecture)
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- [Dataset](#dataset)
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- [Environment Requirements](#environment-requirements)
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- [Quick Start](#quick-start)
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- [Script Description](#script-description)
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- [Script and Sample Code](#script-and-sample-code)
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- [Script Parameters](#script-parameters)
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- [Training Script Parameters](#training-script-parameters)
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- [Preprocess Scripts Parameters](#preprocess-script-parameters)
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- [Dataset Preparation](#dataset-preparation)
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- [Process the Real World Data](#process-the-real-world-data)
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- [Generate and Process the Synthetic Data](#generate-and-process-the-synthetic-data)
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- [Training Process](#training-process)
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- [SingleDevice](#singledevice)
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- [Distribute Training](#distribute-training)
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- [Parameter Server](#parameter-server)
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- [Evaluation Process](#evaluation-process)
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- [Model Description](#model-description)
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- [Performance](#performance)
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- [Training Performance](#training-performance)
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- [Evaluation Performance](#evaluation-performance)
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- [Description of Random Situation](#description-of-random-situation)
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- [ModelZoo Homepage](#modelzoo-homepage)
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WideDeep model jointly trained wide linear models and deep neural network, which combined the benefits of memorization and generalization for recommender systems.
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## Requirements
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# [Wide&Deep Description](#contents)
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Wide&Deep model is a classical model in Recommendation and Click Prediction area. This is an implementation of Wide&Deep as described in the [Wide & Deep Learning for Recommender System](https://arxiv.org/pdf/1606.07792.pdf) paper.
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- Install [MindSpore](https://www.mindspore.cn/install/en).
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# [Model Architecture](#contents)
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Wide&Deep model jointly trained wide linear models and deep neural network, which combined the benefits of memorization and generalization for recommender systems.
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- Place the raw dataset under a certain path, such as: ./recommendation_dataset/origin_data, if you use [criteo dataset](https://s3-eu-west-1.amazonaws.com/kaggle-display-advertising-challenge-dataset/dac.tar.gz), please downlowd the dataset and unzip it to ./recommendation_dataset/origin_data.
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Currently we support host-device mode with column partition and parameter server mode.
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- Convert the dataset to mindrecord, command as follows:
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# [Dataset](#contents)
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- [1] A dataset used in Guo H , Tang R , Ye Y , et al. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction[J]. 2017.
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# [Environment Requirements](#contents)
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- Hardware（Ascend or GPU）
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- Prepare hardware environment with Ascend processor. If you want to try Ascend , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
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- Framework
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- [MindSpore](https://gitee.com/mindspore/mindspore)
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- For more information, please check the resources below：
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- [MindSpore tutorials](https://www.mindspore.cn/tutorial/en/master/index.html)
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- [MindSpore API](https://www.mindspore.cn/api/en/master/index.html)
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# [Quick Start](#contents)
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1. Clone the Code
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```
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python src/preprocess_data.py --data_path=./recommendation_dataset --dense_dim=13 --slot_dim=26 --threshold=100 --train_line_count=45840617 --skip_id_convert=0
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git clone https://gitee.com/mindspore/mindspore.git
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cd mindspore/model_zoo/official/recommend/wide_and_deep
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```
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2. Download the Dataset
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> Please refer to [1] to obtain the download link
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```bash
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mkdir -p data/origin_data && cd data/origin_data
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wget DATA_LINK
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tar -zxvf dac.tar.gz
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```
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3. Use this script to preprocess the data. This may take about one hour and the generated mindrecord data is under data/mindrecord.
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```bash
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python src/preprocess_data.py --data_path=./data/ --dense_dim=13 --slot_dim=26 --threshold=100 --train_line_count=45840617 --skip_id_convert=0
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```
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4. Start Training
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Once the dataset is ready, the model can be trained and evaluated on the single device(Ascend) by the command as follows:
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```bash
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python train_and_eval.py --data_path=./data/mindrecord --data_type=mindrecord
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```
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To evaluate the model, command as follows:
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```bash
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python eval.py --data_path=./data/mindrecord --data_type=mindrecord
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```
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# [Script Description](#contents)
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## [Script and Sample Code](#contents)
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```
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└── wide_and_deep
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├── eval.py
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├── README.md
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├── script
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│ ├── cluster_32p.json
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│ ├── common.sh
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│ ├── deploy_cluster.sh
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│ ├── run_auto_parallel_train_cluster.sh
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│ ├── run_auto_parallel_train.sh
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│ ├── run_multigpu_train.sh
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│ ├── run_multinpu_train.sh
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│ ├── run_parameter_server_train_cluster.sh
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│ ├── run_parameter_server_train.sh
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│ ├── run_standalone_train_for_gpu.sh
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│ └── start_cluster.sh
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├── src
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│ ├── callbacks.py
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│ ├── config.py
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│ ├── datasets.py
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│ ├── generate_synthetic_data.py
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│ ├── __init__.py
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│ ├── metrics.py
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│ ├── preprocess_data.py
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│ ├── process_data.py
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│ └── wide_and_deep.py
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├── train_and_eval_auto_parallel.py
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├── train_and_eval_distribute.py
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├── train_and_eval_parameter_server.py
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├── train_and_eval.py
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└── train.py
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```
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## [Script Parameters](#contents)
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### [Training Script Parameters](#contents)
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The parameters is same for ``train.py``,``train_and_eval.py`` ,``train_and_eval_distribute.py`` and ``train_and_eval_auto_parallel.py``
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```
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Arguments:
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* `--data_path` : The path of the data file.
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* `--dense_dim` : The number of your continues fields.
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* `--slot_dim` : The number of your sparse fields, it can also be called category features.
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* `--threshold` : Word frequency below this value will be regarded as OOV. It aims to reduce the vocab size.
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* `--train_line_count`: The number of examples in your dataset.
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* `--skip_id_convert`: 0 or 1. If set 1, the code will skip the id convert, regarding the original id as the final id.
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usage: train.py [-h] [--device_target {Ascend,GPU}] [--data_path DATA_PATH]
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[--epochs EPOCHS] [--full_batch FULL_BATCH]
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[--batch_size BATCH_SIZE] [--eval_batch_size EVAL_BATCH_SIZE]
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[--field_size FIELD_SIZE] [--vocab_size VOCAB_SIZE]
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[--emb_dim EMB_DIM]
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[--deep_layer_dim DEEP_LAYER_DIM [DEEP_LAYER_DIM ...]]
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[--deep_layer_act DEEP_LAYER_ACT] [--keep_prob KEEP_PROB]
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[--dropout_flag DROPOUT_FLAG] [--output_path OUTPUT_PATH]
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[--ckpt_path CKPT_PATH] [--eval_file_name EVAL_FILE_NAME]
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[--loss_file_name LOSS_FILE_NAME]
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[--host_device_mix HOST_DEVICE_MIX]
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[--dataset_type DATASET_TYPE]
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[--parameter_server PARAMETER_SERVER]
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## Dataset
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The common used benchmark datasets are used for model training and evaluation.
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optional arguments:
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--device_target {Ascend,GPU} device where the code will be implemented. (Default:Ascend)
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--data_path DATA_PATH This should be set to the same directory given to the
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data_download's data_dir argument
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--epochs EPOCHS Total train epochs. (Default:15)
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--full_batch FULL_BATCH Enable loading the full batch. (Default:False)
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--batch_size BATCH_SIZE Training batch size.(Default:16000)
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--eval_batch_size Eval batch size.(Default:16000)
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--field_size The number of features.(Default:39)
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--vocab_size The total features of dataset.(Default:200000)
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--emb_dim The dense embedding dimension of sparse feature.(Default:80)
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--deep_layer_dim The dimension of all deep layers.(Default:[1024,512,256,128])
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--deep_layer_act The activation function of all deep layers.(Default:'relu')
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--keep_prob The keep rate in dropout layer.(Default:1.0)
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--dropout_flag Enable dropout.(Default:0)
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--output_path Deprecated
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--ckpt_path The location of the checkpoint file.(Defalut:./checkpoints/)
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--eval_file_name Eval output file.(Default:eval.og)
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--loss_file_name Loss output file.(Default:loss.log)
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--host_device_mix Enable host device mode or not.(Default:0)
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--dataset_type The data type of the training files, chosen from tfrecord/mindrecord/hd5.(Default:tfrecord)
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--parameter_server Open parameter server of not.(Default:0)
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```
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### [Preprocess Scripts Parameters](#contents)
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```
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usage: generate_synthetic_data.py [-h] [--output_file OUTPUT_FILE]
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[--label_dim LABEL_DIM]
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[--number_examples NUMBER_EXAMPLES]
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[--dense_dim DENSE_DIM]
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[--slot_dim SLOT_DIM]
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[--vocabulary_size VOCABULARY_SIZE]
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[--random_slot_values RANDOM_SLOT_VALUES]
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optional arguments:
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--output_file The output path of the generated file.(Default: ./train.txt)
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--label_dim The label category. (Default:2)
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--number_examples The row numbers of the generated file. (Default:4000000)
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--dense_dim The number of the continue feature.(Default:13)
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--slot_dim The number of the category features.(Default:26)
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--vocabulary_size The vocabulary size of the total dataset.(Default:400000000)
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--random_slot_values 0 or 1. If 1, the id is generated by the random. If 0, the id is set by the row_index mod part_size, where part_size is the vocab size for each slot
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```
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```
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usage: preprocess_data.py [-h]
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[--data_path DATA_PATH] [--dense_dim DENSE_DIM]
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[--slot_dim SLOT_DIM] [--threshold THRESHOLD]
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[--train_line_count TRAIN_LINE_COUNT]
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[--skip_id_convert {0,1}]
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--data_path The path of the data file.
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--dense_dim The number of your continues fields.(default: 13)
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--slot_dim The number of your sparse fields, it can also be called category features.(default: 26)
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--threshold Word frequency below this value will be regarded as OOV. It aims to reduce the vocab size. (default: 100)
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--train_line_count The number of examples in your dataset.
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--skip_id_convert 0 or 1. If set 1, the code will skip the id convert, regarding the original id as the final id.(default: 0)
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```
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## [Dataset Preparation](#contents)
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### [Process the Real World Data](#content)
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### Generate the synthetic Data
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The following command will generate 40 million lines of click data, in the format of "label\tdense_feature[0]\tdense_feature[1]...\tsparse_feature[0]\tsparse_feature[1]...".
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1. Download the Dataset and place the raw dataset under a certain path, such as: ./data/origin_data
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```bash
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mkdir -p data/origin_data && cd data/origin_data
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wget DATA_LINK
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tar -zxvf dac.tar.gz
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```
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> Please refer to [1] to obtain the download link
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2. Use this script to preprocess the data
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```bash
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python src/preprocess_data.py --data_path=./data/ --dense_dim=13 --slot_dim=26 --threshold=100 --train_line_count=45840617 --skip_id_convert=0
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```
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### [Generate and Process the Synthetic Data](#content)
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1. The following command will generate 40 million lines of click data, in the format of
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> "label\tdense_feature[0]\tdense_feature[1]...\tsparse_feature[0]\tsparse_feature[1]...".
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```
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mkdir -p syn_data/origin_data
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python src/generate_synthetic_data.py --output_file=syn_data/origin_data/train.txt --number_examples=40000000 --dense_dim=13 --slot_dim=51 --vocabulary_size=2000000000 --random_slot_values=0
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```
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Arguments:
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* `--output_file`: The output path of the generated file
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* `--label_dim` : The label category
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* `--number_examples`: The row numbers of the generated file
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* `--dense_dim` : The number of the continue feature.
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* `--slot_dim`: The number of the category features
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* `--vocabulary_size`: The vocabulary size of the total dataset
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* `--random_slot_values`: 0 or 1. If 1, the id is generated by the random. If 0, the id is set by the row_index mod part_size, where
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part_size is the vocab size for each slot
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Preprocess the generated data
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2. Preprocess the generated data
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```
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python src/preprocess_data.py --data_path=./syn_data/ --data_type=synthetic --dense_dim=13 --slot_dim=51 --threshold=0 --train_line_count=40000000 --skip_id_convert=1
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python src/preprocess_data.py --data_path=./syn_data/ --dense_dim=13 --slot_dim=51 --threshold=0 --train_line_count=40000000 --skip_id_convert=1
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```
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## [Training Process](#contents)
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### [SingleDevice](#contents)
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## Running Code
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### Code Structure
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The entire code structure is as following:
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```
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|--- wide_and_deep/
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train_and_eval.py "Entrance of Wide&Deep model training and evaluation"
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eval.py "Entrance of Wide&Deep model evaluation"
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train.py "Entrance of Wide&Deep model training"
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train_and_eval_multinpu.py "Entrance of Wide&Deep model data parallel training and evaluation"
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train_and_eval_auto_parallel.py
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train_and_eval_parameter_server.py "Entrance of Wide&Deep model parameter server training and evaluation"
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|--- src/ "Entrance of training and evaluation"
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config.py "Parameters configuration"
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dataset.py "Dataset loader class"
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process_data.py "Process dataset"
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preprocess_data.py "Pre_process dataset"
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wide_and_deep.py "Model structure"
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callbacks.py "Callback class for training and evaluation"
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generate_synthetic_data.py "Generate the synthetic data for benchmark"
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metrics.py "Metric class"
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|--- script/ "Run shell dir"
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run_multinpu_train.sh "Run data parallel"
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run_auto_parallel_train.sh "Run auto parallel"
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run_parameter_server_train.sh "Run parameter server"
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```
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### Train and evaluate model
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To train and evaluate the model, command as follows:
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```
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python train_and_eval.py
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```
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Arguments:
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* `--device_target`: Device where the code will be implemented (Default: Ascend).
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* `--data_path`: This should be set to the same directory given to the data_download's data_dir argument.
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* `--epochs`: Total train epochs.
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* `--batch_size`: Training batch size.
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* `--eval_batch_size`: Eval batch size.
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* `--field_size`: The number of features.
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* `--vocab_size`： The total features of dataset.
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* `--emb_dim`： The dense embedding dimension of sparse feature.
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* `--deep_layers_dim`： The dimension of all deep layers.
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* `--deep_layers_act`： The activation of all deep layers.
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* `--dropout_flag`： Whether do dropout.
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* `--keep_prob`： The rate to keep in dropout layer.
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* `--ckpt_path`：The location of the checkpoint file.
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* `--eval_file_name` : Eval output file.
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* `--loss_file_name` : Loss output file.
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* `--dataset_type` : tfrecord/mindrecord/hd5.
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To train the model in one device, command as follows:
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```
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python train.py
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```
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Arguments:
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* `--device_target`: Device where the code will be implemented (Default: Ascend).
|
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* `--data_path`: This should be set to the same directory given to the data_download's data_dir argument.
|
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* `--epochs`: Total train epochs.
|
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* `--batch_size`: Training batch size.
|
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* `--eval_batch_size`: Eval batch size.
|
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* `--field_size`: The number of features.
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* `--vocab_size`： The total features of dataset.
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* `--emb_dim`： The dense embedding dimension of sparse feature.
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* `--deep_layers_dim`： The dimension of all deep layers.
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* `--deep_layers_act`： The activation of all deep layers.
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* `--dropout_flag`： Whether do dropout.
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* `--keep_prob`： The rate to keep in dropout layer.
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* `--ckpt_path`：The location of the checkpoint file.
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* `--eval_file_name` : Eval output file.
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* `--loss_file_name` : Loss output file.
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* `--dataset_type` : tfrecord/mindrecord/hd5.
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To train the model in distributed, command as follows:
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### [Distribute Training](#contents)
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To train the model in data distributed training, command as follows:
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```
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# configure environment path before training
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bash run_multinpu_train.sh RANK_SIZE EPOCHS DATASET RANK_TABLE_FILE
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```
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To train the model in model parallel training, commands as follows:
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```
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# configure environment path before training
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bash run_auto_parallel_train.sh RANK_SIZE EPOCHS DATASET RANK_TABLE_FILE
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|
|
@ -147,7 +260,7 @@ bash deploy_cluster.sh CLUSTER_CONFIG_PATH EXECUTE_PATH
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bash start_cluster.sh CLUSTER_CONFIG_PATH EPOCH_SIZE VOCAB_SIZE EMB_DIM
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DATASET ENV_SH RANK_TABLE_FILE MODE
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```
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### [Parameter Server](#contents)
|
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To train and evaluate the model in parameter server mode, command as follows:'''
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```
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# SERVER_NUM is the number of parameter servers for this task.
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|
|
@ -157,24 +270,56 @@ To train and evaluate the model in parameter server mode, command as follows:'''
|
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bash run_parameter_server_train.sh RANK_SIZE EPOCHS DATASET RANK_TABLE_FILE SERVER_NUM SCHED_HOST SCHED_PORT
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```
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|
||||
|
||||
|
||||
## [Evaluation Process](#contents)
|
||||
To evaluate the model, command as follows:
|
||||
```
|
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python eval.py
|
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```
|
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Arguments:
|
||||
* `--device_target`: Device where the code will be implemented (Default: Ascend).
|
||||
* `--data_path`: This should be set to the same directory given to the data_download's data_dir argument.
|
||||
* `--epochs`: Total train epochs.
|
||||
* `--batch_size`: Training batch size.
|
||||
* `--eval_batch_size`: Eval batch size.
|
||||
* `--field_size`: The number of features.
|
||||
* `--vocab_size`： The total features of dataset.
|
||||
* `--emb_dim`： The dense embedding dimension of sparse feature.
|
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* `--deep_layers_dim`： The dimension of all deep layers.
|
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* `--deep_layers_act`： The activation of all deep layers.
|
||||
* `--keep_prob`： The rate to keep in dropout layer.
|
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* `--ckpt_path`：The location of the checkpoint file.
|
||||
* `--eval_file_name` : Eval output file.
|
||||
* `--loss_file_name` : Loss output file.
|
||||
|
||||
There are other arguments about models and training process. Use the `--help` or `-h` flag to get a full list of possible arguments with detailed descriptions.
|
||||
# [Model Description](#contents)
|
||||
|
||||
## [Performance](#contents)
|
||||
|
||||
### Training Performance
|
||||
|
||||
| Parameters | Single <br />Ascend | Single<br />GPU | Data-Parallel-8P | Host-Device-mode-8P |
|
||||
| ------------------------ | ------------------------------- | ------------------------------- | ------------------------------- | ------------------------------- |
|
||||
| Resource | Ascend 910 | Tesla V100-PCIE 32G | Ascend 910 | Ascend 910 |
|
||||
| Uploaded Date | 08/21/2020 (month/day/year) | 08/21/2020 (month/day/year) | 08/21/2020 (month/day/year) | 08/21/2020 (month/day/year) |
|
||||
| MindSpore Version | 0.6.0-beta | master | 0.6.0-beta | 0.6.0-beta |
|
||||
| Dataset | [1] | [1] | [1] | [1] |
|
||||
| Training Parameters | Epoch=15,<br />batch_size=16000 | Epoch=15,<br />batch_size=16000 | Epoch=15,<br />batch_size=16000 | Epoch=15,<br />batch_size=16000 |
|
||||
| Optimizer | FTRL,Adam | FTRL,Adam | FTRL,Adam | FTRL,Adam |
|
||||
| Loss Function | SigmoidCrossEntroy | SigmoidCrossEntroy | SigmoidCrossEntroy | SigmoidCrossEntroy |
|
||||
| AUC Score | 0.80937 | 0.80971 | 0.80862 | 0.80834 |
|
||||
| Speed | 20.906 ms/step | 24.465 ms/step | 27.388 ms/step | 236.506 ms/step |
|
||||
| Loss | wide:0.433,deep:0.444 | wide:0.444, deep:0.456 | wide:0.437, deep: 0.448 | wide:0.444, deep:0.444 |
|
||||
| Parms(M) | 75.84 | 75.84 | 75.84 | 75.84 |
|
||||
| Checkpoint for inference | 233MB(.ckpt file) | 230MB(.ckpt) | 233MB(.ckpt file) | 233MB(.ckpt file) |
|
||||
|
||||
|
||||
|
||||
All executable scripts can be found in [here](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/recommend/wide_and_deep/script)
|
||||
|
||||
Note: The result of GPU is tested under the master version. The parameter server mode of the Wide&Deep model is still under development.
|
||||
|
||||
### Evaluation Performance
|
||||
|
||||
| Parameters | Wide&Deep |
|
||||
| ----------------- | --------------------------- |
|
||||
| Resource | Ascend 910 |
|
||||
| Uploaded Date | 08/21/2020 (month/day/year) |
|
||||
| MindSpore Version | 0.6.0-beta |
|
||||
| Dataset | [1] |
|
||||
| Batch Size | 16000 |
|
||||
| Outputs | AUC |
|
||||
| Accuracy | AUC=0.809 |
|
||||
|
||||
# [Description of Random Situation](#contents)
|
||||
|
||||
|
||||
# [ModelZoo Homepage](#contents)
|
||||
|
||||
Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
|
||||
|
|
@ -22,25 +22,28 @@ def argparse_init():
|
|||
parser = argparse.ArgumentParser(description='WideDeep')
|
||||
parser.add_argument("--device_target", type=str, default="Ascend", choices=["Ascend", "GPU"],
|
||||
help="device where the code will be implemented. (Default: Ascend)")
|
||||
parser.add_argument("--data_path", type=str, default="./test_raw_data/")
|
||||
parser.add_argument("--epochs", type=int, default=15)
|
||||
parser.add_argument("--full_batch", type=bool, default=False)
|
||||
parser.add_argument("--batch_size", type=int, default=16000)
|
||||
parser.add_argument("--eval_batch_size", type=int, default=16000)
|
||||
parser.add_argument("--field_size", type=int, default=39)
|
||||
parser.add_argument("--vocab_size", type=int, default=200000)
|
||||
parser.add_argument("--emb_dim", type=int, default=80)
|
||||
parser.add_argument("--deep_layer_dim", type=int, nargs='+', default=[1024, 512, 256, 128])
|
||||
parser.add_argument("--deep_layer_act", type=str, default='relu')
|
||||
parser.add_argument("--keep_prob", type=float, default=1.0)
|
||||
parser.add_argument("--dropout_flag", type=int, default=0)
|
||||
parser.add_argument("--data_path", type=str, default="./test_raw_data/",
|
||||
help="This should be set to the same directory given to the data_download's data_dir argument")
|
||||
parser.add_argument("--epochs", type=int, default=15, help="Total train epochs")
|
||||
parser.add_argument("--full_batch", type=bool, default=False, help="Enable loading the full batch ")
|
||||
parser.add_argument("--batch_size", type=int, default=16000, help="Training batch size.")
|
||||
parser.add_argument("--eval_batch_size", type=int, default=16000, help="Eval batch size.")
|
||||
parser.add_argument("--field_size", type=int, default=39, help="The number of features.")
|
||||
parser.add_argument("--vocab_size", type=int, default=200000, help="The total features of dataset.")
|
||||
parser.add_argument("--emb_dim", type=int, default=80, help="The dense embedding dimension of sparse feature.")
|
||||
parser.add_argument("--deep_layer_dim", type=int, nargs='+', default=[1024, 512, 256, 128],
|
||||
help="The dimension of all deep layers.")
|
||||
parser.add_argument("--deep_layer_act", type=str, default='relu',
|
||||
help="The activation function of all deep layers.")
|
||||
parser.add_argument("--keep_prob", type=float, default=1.0, help="The keep rate in dropout layer.")
|
||||
parser.add_argument("--dropout_flag", type=int, default=0, help="Enable dropout")
|
||||
parser.add_argument("--output_path", type=str, default="./output/")
|
||||
parser.add_argument("--ckpt_path", type=str, default="./checkpoints/")
|
||||
parser.add_argument("--eval_file_name", type=str, default="eval.log")
|
||||
parser.add_argument("--loss_file_name", type=str, default="loss.log")
|
||||
parser.add_argument("--host_device_mix", type=int, default=0)
|
||||
parser.add_argument("--dataset_type", type=str, default="tfrecord")
|
||||
parser.add_argument("--parameter_server", type=int, default=0)
|
||||
parser.add_argument("--ckpt_path", type=str, default="./checkpoints/", help="The location of the checkpoint file.")
|
||||
parser.add_argument("--eval_file_name", type=str, default="eval.log", help="Eval output file.")
|
||||
parser.add_argument("--loss_file_name", type=str, default="loss.log", help="Loss output file.")
|
||||
parser.add_argument("--host_device_mix", type=int, default=0, help="Enable host device mode or not")
|
||||
parser.add_argument("--dataset_type", type=str, default="tfrecord", help="tfrecord/mindrecord/hd5")
|
||||
parser.add_argument("--parameter_server", type=int, default=0, help="Open parameter server of not")
|
||||
return parser
|
||||
|
||||
|
||||
|
|
@ -48,6 +51,7 @@ class WideDeepConfig():
|
|||
"""
|
||||
WideDeepConfig
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self.device_target = "Ascend"
|
||||
self.data_path = "./test_raw_data/"
|
||||
|
|
|
|||
Loading…
Reference in New Issue